bindingAffinity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a66e9f1-acc3-4caf-a6f6-e7ae406313fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0c447eea-fcb0-49cc-b365-6f2390886b78",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import csv\n",
    "import random\n",
    "\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras import regularizers\n",
    "from tensorflow.keras import optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1082013e-c7e4-406b-993e-21ec0b840b2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ea458bbe-6e87-4193-8c7c-3c88df0f434c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9125"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data\n",
    "data=[] # list of lists of the from [similes, sequence, pKd]\n",
    "\n",
    "with open('dataset.csv') as csvfile:\n",
    "    reader = csv.reader(csvfile)\n",
    "    next(reader) #skip header\n",
    "    for row in reader:\n",
    "        triplet=[]\n",
    "        triplet.append(row[0])\n",
    "        triplet.append(row[1])\n",
    "        triplet.append(float(row[2]))\n",
    "        data.append(triplet)\n",
    "\n",
    "len(data)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c10f5311-ca80-48b7-aad7-f7c6eaf5dbc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OCC(CC4=CC=CC=C4)N',\n",
       " 'MKKFFDSRREQGGSGLGSGSSGGGGSTSGLGSGYIGRVFGIGRQQVTVDEVLAEGGFAIVFLVRTSNGMKCALKRMFVNNEHDLQVCKREIQIMRDLSGHKNIVGYIDSSINNVSSGDVWEVLILMDFCRGGQVVNLMNQRLQTGFTENEVLQIFCDTCEAVARLHQCKTPIIHRDLKVENILLHDRGHYVLCDFGSATNKFQNPQTEGVNAVEDEIKKYTTLSYRAPEMVNLYSGKIITTKADIWALGCLLYKLCYFTLPFGESQVAICDGNFTIPDNSRYSQDMHCLIRYMLEPDPDKRPDIYQVSYFSFKLLKKECPIPNVQNSPIPAKLPEPVKASEAAAKKTQPKARLTDPIPTTETSIAPRQRPKAGQTQPNPGILPIQPALTPRKRATVQPPPQAAGSSNQPGLLASVPQPKPQAPPSQPLPQTQAKQPQAPPTPQQTPSTQAQGLPAQAQATPQHQQQLFLKQQQQQQQPPPAQQQPAGTFYQQQQAQTQQFQAVHPATQKPAIAQFPVVSQGGSQQQLMQNFYQQQQQQQQQQQQQQLATALHQQQLMTQQAALQQKPTMAAGQQPQPQPAAAPQPAPAQEPAIQAPVRQQPKVQTTPPPAVQGQKVGSLTPPSSPKTQRAGHRRILSDVTHSAVFGVPASKSTQLLQAAAAEASLNKSKSATTTPSGSPRTSQQNVYNPSEGSTWNPFDDDNFSKLTAEELLNKDFAKLGEGKHPEKLGGSAESLIPGFQSTQGDAFATTSFSAGTAEKRKGGQTVDSGLPLLSVSDPFIPLQVPDAPEKLIEGLKSPDTSLLLPDLLPMTDPFGSTSDAVIEKADVAVESLIPGLEPPVPQRLPSQTESVTSNRTDSLTGEDSLLDCSLLSNPTTDLLEEFAPTAISAPVHKAAEDSNLISGFDVPEGSDKVAEDEFDPIPVLITKNPQGGHSRNSSGSSESSLPNLARSLLLVDQLIDL',\n",
       " 7.366531544]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0]  #looks good!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cd928bdc-b3a8-48a5-a1c5-8fae63b7625c",
   "metadata": {},
   "outputs": [],
   "source": [
    "smiles=[]\n",
    "proteins=[]\n",
    "labels=[]\n",
    "random.shuffle(data)\n",
    "\n",
    "for triplet in range(len(data)):\n",
    "      smiles.append(data[triplet][0])\n",
    "      proteins.append(data[triplet][1])\n",
    "      labels.append(data[triplet][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6812c78a-60d7-4cad-ab5c-54932ffaa56a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CC1=CC2=C(C=C1)N=C(C3=NC=C(N23)C)NCCN.Cl\n",
      "MPLRHWGMARGSKPVGDGAQPMAAMGGLKVLLHWAGPGGGEPWVTFSESSLTAEEVCIHIAHKVGITPPCFNLFALFDAQAQVWLPPNHILEIPRDASLMLYFRIRFYFRNWHGMNPREPAVYRCGPPGTEASSDQTAQGMQLLDPASFEYLFEQGKHEFVNDVASLWELSTEEEIHHFKNESLGMAFLHLCHLALRHGIPLEEVAKKTSFKDCIPRSFRRHIRQHSALTRLRLRNVFRRFLRDFQPGRLSQQMVMVKYLATLERLAPRFGTERVPVCHLRLLAQAEGEPCYIRDSGVAPTDPGPESAAGPPTHEVLVTGTGGIQWWPVEEEVNKEEGSSGSSGRNPQASLFGKKAKAHKAVGQPADRPREPLWAYFCDFRDITHVVLKEHCVSIHRQDNKCLELSLPSRAAALSFVSLVDGYFRLTADSSHYLCHEVAPPRLVMSIRDGIHGPLLEPFVQAKLRPEDGLYLIHWSTSHPYRLILTVAQRSQAPDGMQSLRLRKFPIEQQDGAFVLEGWGRSFPSVRELGAALQGCLLRAGDDCFSLRRCCLPQPGETSNLIIMRGARASPRTLNLSQLSFHRVDQKEITQLSHLGQGTRTNVYEGRLRVEGSGDPEEGKMDDEDPLVPGRDRGQELRVVLKVLDPSHHDIALAFYETASLMSQVSHTHLAFVHGVCVRGPENIMVTEYVEHGPLDVWLRRERGHVPMAWKMVVAQQLASALSYLENKNLVHGNVCGRNILLARLGLAEGTSPFIKLSDPGVGLGALSREERVERIPWLAPECLPGGANSLSTAMDKWGFGATLLEICFDGEAPLQSRSPSEKEHFYQRQHRLPEPSCPQLATLTSQCLTYEPTQRPSFRTILRDLTRLQPHNLADVLTVNPDSPASDPTVFHKRYLKKIRDLGEGHFGKVSLYCYDPTNDGTGEMVAVKALKADCGPQHRSGWKQEIDILRTLYHEHIIKYKGCCEDQGEKSLQLVMEYVPLGSLRDYLPRHSIGLAQLLLFAQQICEGMAYLHAQHYIHRDLAARNVLLDNDRLVKIGDFGLAKAVPEGHEYYRVREDGDSPVFWYAPECLKEYKFYYASDVWSFGVTLYELLTHCDSSQSPPTKFLELIGIAQGQMTVLRLTELLERGERLPRPDKCPCEVYHLMKNCWETEASFRPTFENLIPILKTVHEKYQGQAPSVFSVC\n",
      "5.698970004\n"
     ]
    }
   ],
   "source": [
    "print(smiles[0])\n",
    "print(proteins[0])\n",
    "print(labels[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bfd43928-6648-47a3-8e89-0252c544c119",
   "metadata": {},
   "outputs": [],
   "source": [
    "split=int(0.9*len(smiles))\n",
    "train_smiles = smiles[:split]\n",
    "test_smiles=smiles[split:]\n",
    "train_proteins=proteins[:split]\n",
    "test_proteins=proteins[split:]\n",
    "train_labels=labels[:split]\n",
    "test_labels=labels[split:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "90b6ad16-06c5-4c44-95f1-98d1dac8ed1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#tokenize smiles\n",
    "# train\n",
    "tokenizer_smiles = Tokenizer(char_level=True)\n",
    "tokenizer_smiles.fit_on_texts(train_smiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fd275513-751e-40e7-9423-f20b4d51367b",
   "metadata": {},
   "outputs": [],
   "source": [
    "word_index_smiles = tokenizer_smiles.word_index\n",
    "vocab_size_smiles = len(word_index_smiles)\n",
    "\n",
    "train_sequences_smiles = tokenizer_smiles.texts_to_sequences(train_smiles)\n",
    "train_padded_smiles = pad_sequences(train_sequences_smiles, truncating=\"post\", padding=\"post\",maxlen=85)\n",
    "\n",
    "test_sequences_smiles = tokenizer_smiles.texts_to_sequences(test_smiles)\n",
    "test_padded_smiles = pad_sequences(test_sequences_smiles, truncating=\"post\", padding=\"post\",maxlen=85)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "00351d15-fa7e-4662-a876-40d5dae09639",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n",
      "21\n"
     ]
    }
   ],
   "source": [
    "# Tokenize proteins\n",
    "# train\n",
    "tokenizer_proteins = Tokenizer(char_level=True)\n",
    "tokenizer_proteins.fit_on_texts(train_proteins)\n",
    "\n",
    "word_index_proteins = tokenizer_proteins.word_index\n",
    "vocab_size_proteins = len(word_index_proteins)\n",
    "\n",
    "train_sequences_proteins = tokenizer_proteins.texts_to_sequences(train_proteins)\n",
    "train_padded_proteins = pad_sequences(train_sequences_proteins, truncating=\"post\", padding=\"post\",maxlen=1200)\n",
    "\n",
    "# test\n",
    "test_sequences_proteins = tokenizer_proteins.texts_to_sequences(test_proteins)\n",
    "test_padded_proteins = pad_sequences(test_sequences_proteins, truncating=\"post\", padding=\"post\", maxlen=1200)\n",
    "\n",
    "print(vocab_size_smiles)\n",
    "print(vocab_size_proteins)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "37184dfd-c945-45e8-b444-40b9e4989bf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test smiles array shape: (913, 85)\n",
      "Train smiles array shape: (8212, 85)\n",
      "Test proteins array shape: (913, 1200)\n",
      "Train proteins array shape: (8212, 1200)\n",
      "Test labels array shape: (913,)\n"
     ]
    }
   ],
   "source": [
    "# convert to numpy arrays and check dimensions\n",
    "test_smiles_array = np.array(test_padded_smiles)\n",
    "train_smiles_array = np.array(train_padded_smiles)\n",
    "test_proteins_array = np.array(test_padded_proteins)\n",
    "train_proteins_array = np.array(train_padded_proteins)\n",
    "\n",
    "# convert array to float32 type\n",
    "test_labels_array = np.array(test_labels, dtype=\"float32\")\n",
    "train_labels_array = np.array(train_labels, dtype=\"float32\")\n",
    "\n",
    "print(f\"Test smiles array shape: {test_smiles_array.shape}\")\n",
    "print(f\"Train smiles array shape: {train_smiles_array.shape}\")\n",
    "print(f\"Test proteins array shape: {test_proteins_array.shape}\")\n",
    "print(f\"Train proteins array shape: {train_proteins_array.shape}\")\n",
    "print(f\"Test labels array shape: {test_labels_array.shape}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "05b9114a-2344-4091-80e8-934361a0dcd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                  </span>┃<span style=\"font-weight: bold\"> Output Shape              </span>┃<span style=\"font-weight: bold\">         Param # </span>┃<span style=\"font-weight: bold\"> Connected to               </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">85</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ input_layer_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1200</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">85</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">3,200</span> │ input_layer[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ embedding_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1200</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,816</span> │ input_layer_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ conv1d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">82</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)            │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,416</span> │ embedding[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ conv1d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1197</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,416</span> │ embedding_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ conv1d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">77</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │          <span style=\"color: #00af00; text-decoration-color: #00af00\">12,352</span> │ conv1d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ conv1d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1190</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,448</span> │ conv1d_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ conv1d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">70</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)            │          <span style=\"color: #00af00; text-decoration-color: #00af00\">49,248</span> │ conv1d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ conv1d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1179</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">73,824</span> │ conv1d_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ global_max_pooling1d          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv1d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalMaxPooling1D</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ global_max_pooling1d_1        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv1d_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalMaxPooling1D</span>)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ global_max_pooling1d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│                               │                           │                 │ global_max_pooling1d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)              │         <span style=\"color: #00af00; text-decoration-color: #00af00\">197,632</span> │ concatenate[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)              │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,049,600</span> │ dropout[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)               │         <span style=\"color: #00af00; text-decoration-color: #00af00\">524,800</span> │ dropout_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                 │             <span style=\"color: #00af00; text-decoration-color: #00af00\">513</span> │ dense_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                 \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to              \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m85\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ input_layer_1 (\u001b[38;5;33mInputLayer\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1200\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m85\u001b[0m, \u001b[38;5;34m128\u001b[0m)           │           \u001b[38;5;34m3,200\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ embedding_1 (\u001b[38;5;33mEmbedding\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1200\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │           \u001b[38;5;34m2,816\u001b[0m │ input_layer_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ conv1d (\u001b[38;5;33mConv1D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m82\u001b[0m, \u001b[38;5;34m32\u001b[0m)            │          \u001b[38;5;34m16,416\u001b[0m │ embedding[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ conv1d_3 (\u001b[38;5;33mConv1D\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1197\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │          \u001b[38;5;34m16,416\u001b[0m │ embedding_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ conv1d_1 (\u001b[38;5;33mConv1D\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m77\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │          \u001b[38;5;34m12,352\u001b[0m │ conv1d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ conv1d_4 (\u001b[38;5;33mConv1D\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1190\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m16,448\u001b[0m │ conv1d_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ conv1d_2 (\u001b[38;5;33mConv1D\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m70\u001b[0m, \u001b[38;5;34m96\u001b[0m)            │          \u001b[38;5;34m49,248\u001b[0m │ conv1d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ conv1d_5 (\u001b[38;5;33mConv1D\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1179\u001b[0m, \u001b[38;5;34m96\u001b[0m)          │          \u001b[38;5;34m73,824\u001b[0m │ conv1d_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ global_max_pooling1d          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m96\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ conv1d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "│ (\u001b[38;5;33mGlobalMaxPooling1D\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ global_max_pooling1d_1        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m96\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ conv1d_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "│ (\u001b[38;5;33mGlobalMaxPooling1D\u001b[0m)          │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate (\u001b[38;5;33mConcatenate\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m192\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ global_max_pooling1d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n",
       "│                               │                           │                 │ global_max_pooling1d_1[\u001b[38;5;34m0\u001b[0m]… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)              │         \u001b[38;5;34m197,632\u001b[0m │ concatenate[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ dense[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)              │       \u001b[38;5;34m1,049,600\u001b[0m │ dropout[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ dense_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)               │         \u001b[38;5;34m524,800\u001b[0m │ dropout_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │             \u001b[38;5;34m513\u001b[0m │ dense_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,963,265</span> (7.49 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,963,265\u001b[0m (7.49 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,963,265</span> (7.49 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,963,265\u001b[0m (7.49 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from keras.layers import Input, Embedding, Concatenate, Conv1D, GlobalMaxPooling1D, Dropout, Dense\n",
    "from keras.models import Model\n",
    "from keras import optimizers\n",
    "import tensorflow as tf\n",
    "\n",
    "# Define two input layers\n",
    "smile_input = Input((85,))\n",
    "protein_input = Input((1200,))\n",
    "\n",
    "# Smiles encoder\n",
    "embed_smiles = Embedding(vocab_size_smiles + 1, 128)(smile_input)\n",
    "conv_smiles = Conv1D(32, 4, activation=\"relu\")(embed_smiles)\n",
    "conv_smiles = Conv1D(64, 6, activation=\"relu\")(conv_smiles)\n",
    "conv_smiles = Conv1D(96, 8, activation=\"relu\")(conv_smiles)\n",
    "max_smiles = GlobalMaxPooling1D()(conv_smiles)\n",
    "\n",
    "# Proteins encoder\n",
    "embed_protein = Embedding(vocab_size_proteins + 1, 128)(protein_input)\n",
    "conv_protein = Conv1D(32, 4, activation=\"relu\")(embed_protein)\n",
    "conv_protein = Conv1D(64, 8, activation=\"relu\")(conv_protein)\n",
    "conv_protein = Conv1D(96, 12, activation=\"relu\")(conv_protein)\n",
    "max_protein = GlobalMaxPooling1D()(conv_protein)\n",
    "\n",
    "# concatenate the two encoder outputs\n",
    "concat_layer = Concatenate()([max_smiles, max_protein])\n",
    "\n",
    "# decoder\n",
    "output = Dense(1024, activation=\"relu\")(concat_layer)\n",
    "output = Dropout(0.1)(output)\n",
    "output = Dense(1024, activation=\"relu\")(output)\n",
    "output = Dropout(0.1)(output)\n",
    "output = Dense(512, activation=\"relu\")(output)\n",
    "output = Dense(1)(output)\n",
    "\n",
    "# define a model with a list of two inputs\n",
    "model = Model(inputs=[smile_input, protein_input], outputs=output)\n",
    "\n",
    "# compile model\n",
    "optimizer = optimizers.Adam(learning_rate=0.001)\n",
    "model.compile(loss='mse', optimizer=optimizer, metrics=['mse'])\n",
    "\n",
    "# visualize model\n",
    "#tf.keras.utils.plot_model(model)\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "701eaa09-b0cb-43ce-9c9c-3ec057ed602d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 726ms/step - loss: 21.6201 - mse: 21.6201 - val_loss: 1.3738 - val_mse: 1.3738\n",
      "Epoch 2/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 678ms/step - loss: 1.3658 - mse: 1.3658 - val_loss: 1.0796 - val_mse: 1.0796\n",
      "Epoch 3/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 692ms/step - loss: 1.0127 - mse: 1.0127 - val_loss: 1.0467 - val_mse: 1.0467\n",
      "Epoch 4/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 710ms/step - loss: 0.9865 - mse: 0.9865 - val_loss: 1.0328 - val_mse: 1.0328\n",
      "Epoch 5/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 785ms/step - loss: 0.9701 - mse: 0.9701 - val_loss: 1.0114 - val_mse: 1.0114\n",
      "Epoch 6/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 847ms/step - loss: 0.9311 - mse: 0.9311 - val_loss: 1.0363 - val_mse: 1.0363\n",
      "Epoch 7/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 946ms/step - loss: 0.9286 - mse: 0.9286 - val_loss: 1.0079 - val_mse: 1.0079\n",
      "Epoch 8/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 884ms/step - loss: 0.9057 - mse: 0.9057 - val_loss: 0.9694 - val_mse: 0.9694\n",
      "Epoch 9/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 855ms/step - loss: 0.8830 - mse: 0.8830 - val_loss: 0.9629 - val_mse: 0.9629\n",
      "Epoch 10/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 880ms/step - loss: 0.8740 - mse: 0.8740 - val_loss: 0.9640 - val_mse: 0.9640\n",
      "Epoch 11/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 848ms/step - loss: 0.8636 - mse: 0.8636 - val_loss: 0.9558 - val_mse: 0.9558\n",
      "Epoch 12/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 790ms/step - loss: 0.8742 - mse: 0.8742 - val_loss: 0.9542 - val_mse: 0.9542\n",
      "Epoch 13/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 763ms/step - loss: 0.8553 - mse: 0.8553 - val_loss: 0.9707 - val_mse: 0.9707\n",
      "Epoch 14/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 771ms/step - loss: 0.8620 - mse: 0.8620 - val_loss: 0.9619 - val_mse: 0.9619\n",
      "Epoch 15/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 787ms/step - loss: 0.8574 - mse: 0.8574 - val_loss: 0.9442 - val_mse: 0.9442\n",
      "Epoch 16/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 749ms/step - loss: 0.8460 - mse: 0.8460 - val_loss: 0.9662 - val_mse: 0.9662\n",
      "Epoch 17/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 831ms/step - loss: 0.8525 - mse: 0.8525 - val_loss: 1.0167 - val_mse: 1.0167\n",
      "Epoch 18/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 1s/step - loss: 0.8534 - mse: 0.8534 - val_loss: 0.9865 - val_mse: 0.9865\n",
      "Epoch 19/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 787ms/step - loss: 0.8545 - mse: 0.8545 - val_loss: 1.0257 - val_mse: 1.0257\n",
      "Epoch 20/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 775ms/step - loss: 0.8672 - mse: 0.8672 - val_loss: 1.0788 - val_mse: 1.0788\n",
      "Epoch 21/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 809ms/step - loss: 0.8669 - mse: 0.8669 - val_loss: 0.9753 - val_mse: 0.9753\n",
      "Epoch 22/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 805ms/step - loss: 0.8408 - mse: 0.8408 - val_loss: 1.0254 - val_mse: 1.0254\n",
      "Epoch 23/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 895ms/step - loss: 0.8447 - mse: 0.8447 - val_loss: 1.1611 - val_mse: 1.1611\n",
      "Epoch 24/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 802ms/step - loss: 0.9054 - mse: 0.9054 - val_loss: 0.9857 - val_mse: 0.9857\n",
      "Epoch 25/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 806ms/step - loss: 0.8479 - mse: 0.8479 - val_loss: 1.1302 - val_mse: 1.1302\n",
      "Epoch 26/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 774ms/step - loss: 0.8786 - mse: 0.8786 - val_loss: 1.1036 - val_mse: 1.1036\n",
      "Epoch 27/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 761ms/step - loss: 0.8675 - mse: 0.8675 - val_loss: 1.2300 - val_mse: 1.2300\n",
      "Epoch 28/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 757ms/step - loss: 0.8935 - mse: 0.8935 - val_loss: 1.1103 - val_mse: 1.1103\n",
      "Epoch 29/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 787ms/step - loss: 0.8683 - mse: 0.8683 - val_loss: 1.3360 - val_mse: 1.3360\n",
      "Epoch 30/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 988ms/step - loss: 0.9316 - mse: 0.9316 - val_loss: 1.2799 - val_mse: 1.2799\n",
      "Epoch 31/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 770ms/step - loss: 0.8983 - mse: 0.8983 - val_loss: 1.0498 - val_mse: 1.0498\n",
      "Epoch 32/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 778ms/step - loss: 0.8218 - mse: 0.8218 - val_loss: 0.9346 - val_mse: 0.9346\n",
      "Epoch 33/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 754ms/step - loss: 0.7763 - mse: 0.7763 - val_loss: 0.9512 - val_mse: 0.9512\n",
      "Epoch 34/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 751ms/step - loss: 0.7871 - mse: 0.7871 - val_loss: 1.0716 - val_mse: 1.0716\n",
      "Epoch 35/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 800ms/step - loss: 0.8305 - mse: 0.8305 - val_loss: 1.1342 - val_mse: 1.1342\n",
      "Epoch 36/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 851ms/step - loss: 0.8250 - mse: 0.8250 - val_loss: 0.9943 - val_mse: 0.9943\n",
      "Epoch 37/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 808ms/step - loss: 0.7677 - mse: 0.7677 - val_loss: 0.9735 - val_mse: 0.9735\n",
      "Epoch 38/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 799ms/step - loss: 0.7436 - mse: 0.7436 - val_loss: 1.0388 - val_mse: 1.0388\n",
      "Epoch 39/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 788ms/step - loss: 0.7631 - mse: 0.7631 - val_loss: 0.9316 - val_mse: 0.9316\n",
      "Epoch 40/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 838ms/step - loss: 0.7135 - mse: 0.7135 - val_loss: 0.7545 - val_mse: 0.7545\n",
      "Epoch 41/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 761ms/step - loss: 0.6549 - mse: 0.6549 - val_loss: 0.6951 - val_mse: 0.6951\n",
      "Epoch 42/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 749ms/step - loss: 0.6147 - mse: 0.6147 - val_loss: 0.6643 - val_mse: 0.6643\n",
      "Epoch 43/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 751ms/step - loss: 0.5867 - mse: 0.5867 - val_loss: 0.6879 - val_mse: 0.6879\n",
      "Epoch 44/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 747ms/step - loss: 0.5714 - mse: 0.5714 - val_loss: 0.7139 - val_mse: 0.7139\n",
      "Epoch 45/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 737ms/step - loss: 0.5978 - mse: 0.5978 - val_loss: 0.6717 - val_mse: 0.6717\n",
      "Epoch 46/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 738ms/step - loss: 0.5478 - mse: 0.5478 - val_loss: 0.6722 - val_mse: 0.6722\n",
      "Epoch 47/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 751ms/step - loss: 0.5338 - mse: 0.5338 - val_loss: 0.6821 - val_mse: 0.6821\n",
      "Epoch 48/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 747ms/step - loss: 0.5349 - mse: 0.5349 - val_loss: 0.6276 - val_mse: 0.6276\n",
      "Epoch 49/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 743ms/step - loss: 0.5109 - mse: 0.5109 - val_loss: 0.6277 - val_mse: 0.6277\n",
      "Epoch 50/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m358s\u001b[0m 11s/step - loss: 0.4919 - mse: 0.4919 - val_loss: 0.6755 - val_mse: 0.6755\n",
      "Epoch 51/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1079s\u001b[0m 34s/step - loss: 0.5450 - mse: 0.5450 - val_loss: 0.6593 - val_mse: 0.6593\n",
      "Epoch 52/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 2s/step - loss: 0.5111 - mse: 0.5111 - val_loss: 0.6472 - val_mse: 0.6472\n",
      "Epoch 53/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 2s/step - loss: 0.5102 - mse: 0.5102 - val_loss: 0.6195 - val_mse: 0.6195\n",
      "Epoch 54/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 2s/step - loss: 0.4837 - mse: 0.4837 - val_loss: 0.6552 - val_mse: 0.6552\n",
      "Epoch 55/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 2s/step - loss: 0.4754 - mse: 0.4754 - val_loss: 0.6664 - val_mse: 0.6664\n",
      "Epoch 56/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 2s/step - loss: 0.4822 - mse: 0.4822 - val_loss: 0.7195 - val_mse: 0.7195\n",
      "Epoch 57/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 2s/step - loss: 0.4917 - mse: 0.4917 - val_loss: 0.6211 - val_mse: 0.6211\n",
      "Epoch 58/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 2s/step - loss: 0.4685 - mse: 0.4685 - val_loss: 0.6011 - val_mse: 0.6011\n",
      "Epoch 59/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 2s/step - loss: 0.4295 - mse: 0.4295 - val_loss: 0.5890 - val_mse: 0.5890\n",
      "Epoch 60/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 2s/step - loss: 0.4258 - mse: 0.4258 - val_loss: 0.7114 - val_mse: 0.7114\n",
      "Epoch 61/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 2s/step - loss: 0.4624 - mse: 0.4624 - val_loss: 0.6844 - val_mse: 0.6844\n",
      "Epoch 62/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 2s/step - loss: 0.4392 - mse: 0.4392 - val_loss: 0.7995 - val_mse: 0.7995\n",
      "Epoch 63/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 2s/step - loss: 0.4770 - mse: 0.4770 - val_loss: 0.6592 - val_mse: 0.6592\n",
      "Epoch 64/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 2s/step - loss: 0.4513 - mse: 0.4513 - val_loss: 0.8102 - val_mse: 0.8102\n",
      "Epoch 65/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 2s/step - loss: 0.5301 - mse: 0.5301 - val_loss: 0.5848 - val_mse: 0.5848\n",
      "Epoch 66/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 2s/step - loss: 0.5537 - mse: 0.5537 - val_loss: 0.6800 - val_mse: 0.6800\n",
      "Epoch 67/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 2s/step - loss: 0.5008 - mse: 0.5008 - val_loss: 0.5892 - val_mse: 0.5892\n",
      "Epoch 68/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 2s/step - loss: 0.3992 - mse: 0.3992 - val_loss: 0.5808 - val_mse: 0.5808\n",
      "Epoch 69/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 2s/step - loss: 0.3658 - mse: 0.3658 - val_loss: 0.5587 - val_mse: 0.5587\n",
      "Epoch 70/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 2s/step - loss: 0.3451 - mse: 0.3451 - val_loss: 0.6049 - val_mse: 0.6049\n",
      "Epoch 71/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 2s/step - loss: 0.3596 - mse: 0.3596 - val_loss: 0.5674 - val_mse: 0.5674\n",
      "Epoch 72/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 2s/step - loss: 0.3388 - mse: 0.3388 - val_loss: 0.5986 - val_mse: 0.5986\n",
      "Epoch 73/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 2s/step - loss: 0.3484 - mse: 0.3484 - val_loss: 0.5465 - val_mse: 0.5465\n",
      "Epoch 74/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 2s/step - loss: 0.3242 - mse: 0.3242 - val_loss: 0.8580 - val_mse: 0.8580\n",
      "Epoch 75/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 2s/step - loss: 0.4035 - mse: 0.4035 - val_loss: 1.0347 - val_mse: 1.0347\n",
      "Epoch 76/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 2s/step - loss: 0.4671 - mse: 0.4671 - val_loss: 0.6418 - val_mse: 0.6418\n",
      "Epoch 77/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 2s/step - loss: 0.4960 - mse: 0.4960 - val_loss: 0.6235 - val_mse: 0.6235\n",
      "Epoch 78/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 2s/step - loss: 0.3699 - mse: 0.3699 - val_loss: 0.6494 - val_mse: 0.6494\n",
      "Epoch 79/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 2s/step - loss: 0.3393 - mse: 0.3393 - val_loss: 0.5887 - val_mse: 0.5887\n",
      "Epoch 80/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 2s/step - loss: 0.3309 - mse: 0.3309 - val_loss: 0.5356 - val_mse: 0.5356\n",
      "Epoch 81/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 2s/step - loss: 0.3117 - mse: 0.3117 - val_loss: 0.6991 - val_mse: 0.6991\n",
      "Epoch 82/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 2s/step - loss: 0.3530 - mse: 0.3530 - val_loss: 0.5356 - val_mse: 0.5356\n",
      "Epoch 83/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 2s/step - loss: 0.3681 - mse: 0.3681 - val_loss: 0.5906 - val_mse: 0.5906\n",
      "Epoch 84/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 2s/step - loss: 0.4026 - mse: 0.4026 - val_loss: 0.8245 - val_mse: 0.8245\n",
      "Epoch 85/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 2s/step - loss: 0.4830 - mse: 0.4830 - val_loss: 0.5908 - val_mse: 0.5908\n",
      "Epoch 86/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 2s/step - loss: 0.2800 - mse: 0.2800 - val_loss: 0.5064 - val_mse: 0.5064\n",
      "Epoch 87/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 2s/step - loss: 0.2600 - mse: 0.2600 - val_loss: 0.5236 - val_mse: 0.5236\n",
      "Epoch 88/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 2s/step - loss: 0.2672 - mse: 0.2672 - val_loss: 0.5166 - val_mse: 0.5166\n",
      "Epoch 89/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 2s/step - loss: 0.2704 - mse: 0.2704 - val_loss: 0.5837 - val_mse: 0.5837\n",
      "Epoch 90/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 2s/step - loss: 0.2653 - mse: 0.2653 - val_loss: 0.6496 - val_mse: 0.6496\n",
      "Epoch 91/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 2s/step - loss: 0.2928 - mse: 0.2928 - val_loss: 0.5124 - val_mse: 0.5124\n",
      "Epoch 92/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 2s/step - loss: 0.2550 - mse: 0.2550 - val_loss: 0.5162 - val_mse: 0.5162\n",
      "Epoch 93/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 2s/step - loss: 0.2391 - mse: 0.2391 - val_loss: 0.5323 - val_mse: 0.5323\n",
      "Epoch 94/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 2s/step - loss: 0.2340 - mse: 0.2340 - val_loss: 0.7510 - val_mse: 0.7510\n",
      "Epoch 95/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 2s/step - loss: 0.3149 - mse: 0.3149 - val_loss: 0.5176 - val_mse: 0.5176\n",
      "Epoch 96/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 2s/step - loss: 0.2635 - mse: 0.2635 - val_loss: 0.5153 - val_mse: 0.5153\n",
      "Epoch 97/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 2s/step - loss: 0.2284 - mse: 0.2284 - val_loss: 0.5168 - val_mse: 0.5168\n",
      "Epoch 98/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 2s/step - loss: 0.2146 - mse: 0.2146 - val_loss: 0.5827 - val_mse: 0.5827\n",
      "Epoch 99/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 2s/step - loss: 0.2418 - mse: 0.2418 - val_loss: 0.5412 - val_mse: 0.5412\n",
      "Epoch 100/100\n",
      "\u001b[1m33/33\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 2s/step - loss: 0.2499 - mse: 0.2499 - val_loss: 0.5137 - val_mse: 0.5137\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "early_stopping = EarlyStopping(\n",
    "    min_delta=0.001,  # minimum amount of change to count as an improvement\n",
    "    patience=10,  # how many epochs to wait before stopping\n",
    "    restore_best_weights=True,\n",
    ")\n",
    "\n",
    "history = model.fit(\n",
    "    [train_smiles_array,train_proteins_array],\n",
    "    train_labels_array,\n",
    "    epochs=100,\n",
    "    batch_size=256,\n",
    "    validation_data=([test_smiles_array, test_proteins_array],test_labels_array)\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "873c0588-6d65-4cc2-9d14-8b974640836b",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'accuracy'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[27], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m model\u001b[38;5;241m.\u001b[39msave(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdta_model.keras\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m accuracy \u001b[38;5;241m=\u001b[39m \u001b[43mhistory\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhistory\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43maccuracy\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[0;32m      3\u001b[0m val_accuracy \u001b[38;5;241m=\u001b[39m history\u001b[38;5;241m.\u001b[39mhistory[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_accuracy\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# Print the accuracy\u001b[39;00m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'accuracy'"
     ]
    }
   ],
   "source": [
    "model.save('dta_model.keras')\n",
    "accuracy = history.history['accuracy']\n",
    "val_accuracy = history.history['val_accuracy']\n",
    "\n",
    "# Print the accuracy\n",
    "print(\"Training Accuracy for each epoch:\", accuracy)\n",
    "print(\"Validation Accuracy for each epoch:\", val_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "01b8a468-aa20-40fa-89e0-771a0c2e6d48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Retrieve a list of list results on training and test sets for each training epoch\n",
    "mse = history.history['mse']\n",
    "val_mse = history.history['val_mse']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(mse))  # Get the number of epochs\n",
    "# Plot training and validation MSE\n",
    "plt.plot(epochs, mse, 'r')\n",
    "plt.plot(epochs, val_mse, 'b')\n",
    "plt.title('Training and Validation MSE')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('MSE')\n",
    "plt.legend(['Training MSE', 'Validation MSE'])\n",
    "plt.figure()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "33f8bd79-6ceb-4e8a-8292-e5ac68fda1a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_pKd(drug, protein):\n",
    "    drug_sequence = tokenizer_smiles.texts_to_sequences([drug])\n",
    "    drug_padded = pad_sequences(drug_sequence, truncating=\"post\", padding=\"post\", maxlen=85)\n",
    "    \n",
    "    protein_sequence = tokenizer_proteins.texts_to_sequences([protein])\n",
    "    protein_padded = pad_sequences(protein_sequence, truncating=\"post\", padding=\"post\", maxlen=1200)\n",
    "    \n",
    "    prediction = model.predict([tf.expand_dims(drug_padded, axis=-1), tf.expand_dims(protein_padded, axis=-1)])\n",
    "    return prediction.item()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "212676d5-e591-4319-ba3f-6e1a1811d954",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[29], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m drug \u001b[38;5;241m=\u001b[39m \u001b[43mtest_smiles\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[0;32m      2\u001b[0m protein \u001b[38;5;241m=\u001b[39m test_proteins[\u001b[38;5;241m1000\u001b[39m]\n\u001b[0;32m      3\u001b[0m label \u001b[38;5;241m=\u001b[39m test_labels[\u001b[38;5;241m1000\u001b[39m]\n",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "drug = test_smiles[1000]\n",
    "protein = test_proteins[1000]\n",
    "label = test_labels[1000]\n",
    "\n",
    "print(label)\n",
    "predict_pKd(drug=drug, protein=protein)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "608fc30d-2247-479c-b544-83f30a95f603",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}